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Systems and methods for predictive network modeling for computational systems, biology and drug target discovery

a network modeling and computational system technology, applied in the field of predictive network modeling, can solve the problems of increasing cardiovascular disease risk and their contribution to explain the mechanisms leading to the decrease of insulin sensitivity, and achieve the effects of enhancing bayesian network learning and accurate prediction of the marginal probability of variables

Pending Publication Date: 2021-01-07
THE ARIZONA BOARD OF REGENTS ON BEHALF OF THE UNIV OF ARIZONA +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Benefits of technology

The invention is a new method that combines two learning algorithms to better predict the cause of diseases like type 2 diabetes. The method uses a special technique called a transformed signal space to approximate the underlying model of the disease. This approach also integrates a new method for inferring causality based on the interactions of genes and other factors. The method can help identify new genes and pathways that contribute to insulin resistance, which is a precursor to diabetes. The invention is useful for exploring the genetic architecture of insulin resistance and developing new treatments for the disease.

Problems solved by technology

Secondly, previously derived constraints over conditional probability distribution reconcile the Bottom-up probabilistic inference in a Bayesian framework with the causality inference problem in continuous signal space.
Insulin resistance (IR) precedes the development of type 2 diabetes (T2D) and increases cardiovascular disease risk.
Although genome wide association studies (GWAS) have uncovered new loci associated with T2D, their contribution to explain the mechanisms leading to decreased insulin sensitivity has been very limited.

Method used

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  • Systems and methods for predictive network modeling for computational systems, biology and drug target discovery
  • Systems and methods for predictive network modeling for computational systems, biology and drug target discovery
  • Systems and methods for predictive network modeling for computational systems, biology and drug target discovery

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example 1

Predictive Network Analysis Identifies HSPA2 as a Novel Alzheimer's Disease Target

[0093]It is believed that changes in gene and protein expression are crucial to the development of late onset Alzheimer's disease (LOAD). Herein, proteins are examined and incorporated into networks in two separate series, and the outputs are evaluated in two different cell lines. The pipeline included the following steps: (1) Predicting expression quantitative trait loci (eQTLs); (2) Determining differential expression; (3) Analyzing networks of transcript and peptide relationships; and (4) Validating effects in two separate cell lines. We performed all the analysis in two separate brain series to validate effects. The two series included 345 samples in the first set (177 controls, 168 cases; age range 65-105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 MCI; age range 66-107; 63% female; RUSH cohort). The top target is Heat Shock Protein Family A Mem...

example 2

Insulin Sensitivity Measurement And IPSC Generation

[0108]The systems and methods of the present invention are also applicable to predictive network modeling in human induced pluripotent stem cells to identify key driver genes for insulin responsiveness.

[0109]Individuals in the study have accompanying genome-wide genotyping and gold standard measurement of insulin sensitivity (i.e. steady state plasma glucose-SSPG-derived from an insulin suppression test). Other biometric parameters include age, body mass index, sex and race / ethnicity (Table 1). Three to seven iPSC lines were generated from each individual, with no apparent differences in the reprogramming efficiency between IR and IS cells. The complete pipeline for iPSC generation and quality control has been previously described. Briefly, RNA-seq data for 317 ipSC lines from 101 individuals was generated and, after quality control, RNA-seq data from 310 samples from 100 individuals was analyzed, of which 48 were IS (149 samples) a...

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Abstract

Systems and methods for predictive network modeling are disclosed. The systems and methods disclosed compute a top-down causal model and a bottom-up predictive model and utilize those models to determine the conditional independence among multiple variables and causality among equivalent variable structures. Before or during modeling, the data is passed through Markov Chain Monte Carlo sampling.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS[0001]This application claims the benefit of U.S. Provisional App. No. 62 / 635,946, filed on Feb. 27, 2018, the entire contents of which are hereby incorporated by reference.GOVERNMENT LICENSE RIGHTS[0002]This invention was made with government support under Grant Nos. NIA 1 RF1 AG057457-01 and NIH / NIDDK P30DK116074 awarded by the National Institute of Health. The government has certain rights to the invention.FIELD OF INVENTION[0003]The present invention relates to computer-implemented systems and methods related to predictive network modeling or top-down & bottom-up predictive network modeling. More specifically, the present invention relates to predictive network modeling for applications in computer networks, the biological sciences, and in drug target discovery.BACKGROUND OF THE RELATED ART[0004]The problem of inferring causality between variables, especially recovering causal networks from observation data is a particularly challenging tas...

Claims

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Application Information

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IPC IPC(8): G16B5/20G16B30/00G16B40/00G06N7/00G06N20/00
CPCG16B5/20G16B30/00G06N20/00G06N7/005G16B40/00G06N5/045G06N7/01
Inventor CHANG, RUISCHADT, ERIC
Owner THE ARIZONA BOARD OF REGENTS ON BEHALF OF THE UNIV OF ARIZONA
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